Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.

Objective: The coronavirus disease 2019 (COVID-19) health crisis that began at the end of 2019 made researchers around the world quickly race to find effective solutions. Related literature exploded and it was inevitable that an automated approach was needed to find useful information, namely text mining, to overcome COVID-19, especially in terms of drug candidate discovery. While text mining methods for finding drug candidates mostly try to extract bioentity associations from PubMed, very few of them mine with a clustering approach.

Network subgraph-based approach for analyzing and comparing molecular networks.

Molecular networks are built up from genetic elements that exhibit feedback interactions. Here, we studied the problem of measuring the similarity of directed networks by proposing a novel alignment-free approach: the network subgraph-based approach. Our approach does not make use of randomized networks to determine modular patterns embedded in a network, and this method differs from the network motif and graphlet methods.

Multi-omics analyses provide novel biological insights to distinguish lobular ductal types of invasive breast cancers.

Background: Invasive lobular carcinoma (ILC) treatment is similar to invasive ductal carcinoma (IDC; now invasive carcinoma-no special type, IBC-NST), based on its intrinsic subtype. However, further investigation is required for an integrative understanding of differentially perturbed molecular patterns and pathways in these histotypes.

Methods: A dataset of 780 IDC and 201 ILC samples from the TCGA-BRCA project for cross-platform multi-omics was analyzed.

A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles.

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach.

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients.

Dissecting molecular network structures using a network subgraph approach.

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs.

Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method.

Cancer is a highly complex disease caused by multiple genetic factors. MicroRNA (miRNA) and mRNA expression profiles are useful for identifying prognostic biomarkers for cancer. Kidney renal clear cell carcinoma (KIRC), which accounts for more than 70% of all renal malignant tumour cases, was selected for our analysis.

A database of integrated molecular and phytochemical interactions of the foxm1 pathway for lung cancer.

The FoxM1 pathway is an oncogenic signaling pathway involved in essential mechanisms including control cell-cycle progression, apoptosis and cell growth which are the common hallmarks of various cancers. Although its biological functions in the tumor development and progression are known, the mechanism by which it participates in those processes is not understood. The present work reveals images of the oncogenic FoxM1 pathway controlling the cell cycle process with alternative treatment options via phytochemical substances in the lung cancer study.

Physalin A attenuates inflammation through down-regulating c-Jun NH2 kinase phosphorylation/Activator Protein 1 activation and up-regulating the antioxidant activity.

Physalin A (PA), a withanolide, was isolated from Physalis angulata L. In this study, it is shown that PA can inhibit the production of inflammatory cytokines such as PGE, NO, IL-1β, IL-6, and TNF-α in LPS-induced RAW 264.7 cells.

Autonomy-Supportive Teaching and Basic Psychological Need Satisfaction among School Students: The Role of Mindfulness.

Grounded in self-determination theory, the purpose of this study was to investigate the relationships between autonomy-supportive teaching, mindfulness, and basic psychological need satisfaction/frustration. Secondary school students ( = 390, age = 15) responded to a survey form measuring psychological constructs pertaining to the research purpose. A series of multiple regression analysis showed that autonomy-supportive teaching and mindfulness positively predicted need satisfaction and negatively predicted need frustration.

Drug repurposing and therapeutic anti-microRNA predictions for inhibition of oxidized low-density lipoprotein-induced vascular smooth muscle cell-associated diseases.

Drug repurposing is a new method for disease treatments, which accelerates the identification of new uses for existing drugs with minimal side effects for patients. MicroRNA-based therapeutics are a class of drugs that have been used in gene therapy following the FDA's approval of the first anti-sense therapy. This study examines the effects of oxLDL on vascular smooth muscle cells (VSMCs) and identifies potential drugs and antimiRs for treating VSMC-associated diseases.

DNA methylation-regulated microRNA pathways in ovarian serous cystadenocarcinoma: A meta-analysis.

Epigenetic regulation has been linked to the initiation and progression of cancer. Aberrant expression of microRNAs (miRNAs) is one such mechanism that can activate or silence oncogenes (OCGs) and tumor suppressor genes (TSGs) in cells. A growing number of studies suggest that miRNA expression can be regulated by methylation modification, thus triggering cancer development.

Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells.

Background: Abnormal proliferation of vascular smooth muscle cells (VSMC) is a major cause of cardiovascular diseases (CVDs). Many studies suggest that vascular injury triggers VSMC dedifferentiation, which results in VSMC changes from a contractile to a synthetic phenotype; however, the underlying molecular mechanisms are still unclear.

Methods: In this study, we examined how VSMC responds under mechanical stress by using time-course microarray data.

Graph theory and stability analysis of protein complex interaction networks.

Protein complexes play an essential role in many biological processes. Complexes can interact with other complexes to form protein complex interaction network (PCIN) that involves in important cellular processes. There are relatively few studies on examining the interaction topology among protein complexes; and little is known about the stability of PCIN under perturbations.

Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory.

Background: Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects.

Results: This work integrates two approaches--machine learning algorithms and topological parameter-based classification--to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments.

FARE-CAFE: a database of functional and regulatory elements of cancer-associated fusion events.

Chromosomal translocation (CT) is of enormous clinical interest because this disorder is associated with various major solid tumors and leukemia. A tumor-specific fusion gene event may occur when a translocation joins two separate genes. Currently, various CT databases provide information about fusion genes and their genomic elements.

Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.

Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types.

Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks.

Background: Molecular networks are the basis of biological processes. Such networks can be decomposed into smaller modules, also known as network motifs. These motifs show interesting dynamical behaviors, in which co-operativity effects between the motif components play a critical role in human diseases.

Drug repositioning discovery for early- and late-stage non-small-cell lung cancer.

Drug repositioning is a popular approach in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development time. Non-small-cell lung cancer (NSCLC) is one of the leading causes of death worldwide. To reduce the biological heterogeneity effects among different individuals, both normal and cancer tissues were taken from the same patient, hence allowing pairwise testing.

In silico identification of potential targets and drugs for non-small cell lung cancer.

Lung cancer is one of the leading causes of death in both the USA and Taiwan, and it is thought that the cause of cancer could be because of the gain of function of an oncoprotein or the loss of function of a tumour suppressor protein. Consequently, these proteins are potential targets for drugs. In this study, differentially expressed genes are identified, via an expression dataset generated from lung adenocarcinoma tumour and adjacent non-tumour tissues.

A model comparison study of the flowering time regulatory network in Arabidopsis.

Background: Several dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses.Two major issues were addressed in this study.

Disturbance of Arabidopsis thaliana microRNA-regulated pathways by Xcc bacterial effector proteins.

Plants are continuously subjected to infection by pathogens, including bacteria and viruses. Bacteria can inject a variety of effector proteins into the host to reprogram host defense mechanism. It is known that microRNAs participate in plant disease resistance to bacterial pathogens and previous studies have suggested that some bacterial effectors have evolved to disturb the host's microRNA-regulated pathways; and so enabling infection.

Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms.

MicroRNAs are small, endogenous RNAs found in many different species and are known to have an influence on diverse biological phenomena. They also play crucial roles in plant biological processes, such as metabolism, leaf sidedness and flower development. However, the functional roles of most microRNAs are still unknown.

Toll-like receptor 9 SNPs are susceptible to the development and progression of membranous glomerulonephritis: 27 years follow-up in Taiwan.

The purpose of this study was to determine whether toll-like receptors 9 (TLR9) gene polymorphisms (rs352139 and rs352140) were markers of susceptibility to the development and progression of membranous nephropathy (MGN) in Taiwanese patients. The polymorphisms were investigated by polymerase chain reaction in 397 Taiwanese individuals (134 MGN patients and 263 controls). Patients with malignancy, chronic infectious diseases, lupus nephritis, or drug-induced secondary MGN were excluded from the study.

Improving protein complex classification accuracy using amino acid composition profile.

Protein complex prediction approaches are based on the assumptions that complexes have dense protein-protein interactions and high functional similarity between their subunits. We investigated those assumptions by studying the subunits' interaction topology, sequence similarity and molecular function for human and yeast protein complexes. Inclusion of amino acids' physicochemical properties can provide better understanding of protein complex properties.